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Modern cars are equipped with many sensors that measure information about the vehicle and its surroundings. These measurements are therefore related to the ride-surface conditions over which the vehicle is passing. The paper commences by outlining a four-component vision for performing road condition evaluation based on in-vehicle sensor readings and subsequent feeding of pavement management systems (PMSs) with live condition information. This is expected to enrich the functionalities of PMSs, and ultimately lead to improved maintenance and repair decisions. Next the LiRA (Live Road Assessment) project—an ongoing proof-of-concept attempt to realize the vision components—is presented. The project elements and software architecture are described in detail, listing any assumptions, compromises, and challenges. LiRA involves a fleet of 400 electric cars operating in Copenhagen, both within the city streets and nearby highways. Sensor data collection is performed with a customized Internet of Things (IoT) device installed in the cars. Data processing and structuring involve new software tools for quality control, spatio-temporal interpolation, and map matching. A hybrid approach, combining machine learning models with physical (mechanics-based) models, is currently being applied to convert data into relevant information for PMSs. Based on the experience thus far with LiRA, the vision actualization is deemed achievable, workable, and up-scalable.

In this article, we consider the problem of planning preventive maintenance of railway signals in Denmark. This case is particularly relevant as the entire railway signalling system is currently being upgraded to the new European Railway Traffic Management System (ERTMS) standard. This upgrade has significant implications for signal maintenance scheduling in the system. We formulate the problem as a multi-depot vehicle routing and scheduling problem with time windows and synchronisation constraints, in a multi-day time schedule. The requirement that some tasks require the simultaneous presence of more than one engineer means that task synchronisation must be considered. A multi-stage constructive framework is proposed, which first distributes maintenance tasks using a clustering formulation. Following this, a Constraint Programming (CP) based approach is used to generate feasible monthly plans for large instances of practical interest. Experimental results indicate that the proposed framework can generate feasible solutions and schedule a monthly plan of up to 1000 tasks for eight crew members, in a reasonable amount of computational time.

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Botnets have become increasingly common and progressively dangerous to both business and domestic networks alike. Due to the Covid-19 pandemic, a large quantity of the population has been performing corporate activities from their homes. This leads to speculation that most computer users and employees working remotely do not have proper defences against botnets, resulting in botnet infection propagating to other devices connected to the target network. Consequently, not only did botnet infection occur within the target user’s machine but also neighbouring devices. The focus of this paper is to review and investigate current state of the art and research works for both methods of infection, such as how a botnet could penetrate a system or network directly or indirectly, and standard detection strategies that had been used in the past. Furthermore, we investigate the capabilities of Artificial Intelligence (AI) to create innovative approaches for botnet detection to enable making predictions as to whether there are botnets present within a network. The paper also discusses methods that threat-actors may be used to infect target devices with botnet code. Machine learning algorithms are examined to determine how they may be used to assist AI-based detection and what advantages and disadvantages they would have to compare the most suitable algorithm businesses could use. Finally, current botnet prevention and countermeasures are discussed to determine how botnets can be prevented from corporate and domestic networks and ensure that future attacks can be prevented.